Sander David, Grandjean Didier, Scherer Klaus R
Geneva Emotion Research Group, Department of Psychology, University of Geneva, 40, Bd. du Pont d'Arve, CH-1205, Geneva, Switzerland.
Neural Netw. 2005 May;18(4):317-52. doi: 10.1016/j.neunet.2005.03.001.
While artificial neural networks are regularly employed in modeling the perception of facial and vocal emotion expression as well as in automatic expression decoding by artificial agents, this approach is yet to be extended to the modeling of emotion elicitation and differentiation. In part, this may be due to the dominance of discrete and dimensional emotion models, which have not encouraged computational modeling. This situation has changed with the advent of appraisal theories of emotion and a number of attempts to develop rule-based models can be found in the literature. However, most of these models operate at a high level of conceptual abstraction and rarely include the underlying neural architecture. In this contribution, an appraisal-based emotion theory, the Component Process Model (CPM), is described that seems particularly suited to modeling with the help of artificial neural network approaches. This is due to its high degree of specificity in postulating underlying mechanisms including efferent physiological and behavioral manifestations as well as to the possibility of linking the theoretical assumptions to underlying neural architectures and dynamic processes. This paper provides a brief overview of the model, suggests constraints imposed by neural circuits, and provides examples on how the temporal unfolding of emotion can be conceptualized and experimentally tested. In addition, it is shown that the specific characteristics of emotion episodes can be profitably explored with the help of non-linear dynamic systems theory.
虽然人工神经网络经常用于对面部和声音情感表达的感知进行建模，以及用于人工智能体的自动表情解码，但这种方法尚未扩展到情感诱发和区分的建模。部分原因可能是离散和维度情感模型占主导地位，它们并不鼓励计算建模。随着情感评估理论的出现，这种情况已经发生了变化，文献中可以找到一些开发基于规则模型的尝试。然而，这些模型大多在高度概念抽象层面上运行，很少包括底层的神经架构。在本论文中，描述了一种基于评估的情感理论——成分过程模型（CPM），它似乎特别适合借助人工神经网络方法进行建模。这是因为它在假设包括传出生理和行为表现的潜在机制方面具有高度特异性，以及将理论假设与底层神经架构和动态过程相联系的可能性。本文简要概述了该模型，提出了神经回路施加的限制，并提供了关于如何将情感的时间展开概念化和进行实验测试的示例。此外，研究表明借助非线性动态系统理论可以有益地探索情感事件的特定特征。
